The present research is focused on the application of hyperspectral images for the supervision of quality deterioration in ready to use leafy spinach during storage (Spinacia oleracea). Two sets of samples of packed leafy spinach were considered: a) first set of samples was stored at 20°C (E-20) in order to accelerate the degradation process; these samples were measured the day of reception in the laboratory and after two days of storage; b) second set of samples was kept at 10°C (E-10); the measurements were taken throughout the period of storage, beginning the day of reception and repeating the acquisition of images three, six and nine days after. Twenty leaves per testing date of test were analyzed. Hyperspectral images were acquired with a push-broom CCD camera equipped with a spectrograph VNIR (400 to 1000 nm). Calibration set of spectra was extracted from E-20 samples, containing three classes of degradation: class A (optimal quality), class B and class C (maximum deterioration). Reference average spectra were defined for each class. Three models, computed on the calibration set, with a decreasing degree of complexity were compared, according to their ability for segregating spinaches at different quality stages (fresh, with incipient and non visible symptoms of degradation, and degraded): spectral angle mapper distance (SAM), partial least squares discriminant analysis models (PLS-DA), and a non linear index (Leafy Vegetable Evolution, LEVE) combining five wavelengths included among the previously selected by CovSel procedure. In sets E-10 and E-20, artificial images of the membership degree according to the distance of each pixel to the reference classes, were computed assigning each pixel to the closest reference class. The three methods were able to show the evolution degradation of the leaves along the time

The present research is focused on the application of artificial vision to assess the ripening of red skinned soft-flesh peach (‘Richlady’). Artificial vision allows a spatially detailed determination of the ripening stage of the fruit. The considered optical indexes (Ind 1 and Ind 2 , proposed in the present research, and Ind 3 and I AD , proposed by other authors) are based on the combination of wavelengths close to the chlorophyll absorption peak at 680 nm. Ind 1 corresponds approximately to the depth of the absorption peak, and Ind 2 corresponds to the relative absorption peak. An artificial image of each index was obtained by computing the corresponding reflectance images, which were acquired with a hyperspectral camera. All indexes were able to correct convexity (except for the just-harvested peaches and for Ind 1 ). Ind 2 is the preferred index; it showed the highest discriminating power between ripening stages and no influence of convexity. Ind 2 also allowed the differentiation of ripening regions within the fruits, and it showed the evolution of those regions during ripening.

The addition of plastic substances in teabags is of increasing concern for conscious consumers due to the harmful effects on the environment and the potential threats to human health. This work introduces an innovative and cost-effective approach to detect and quantify plastic substances in teabags by applying near infrared hyperspectral imaging (951-2496 nm) coupled with multivariate analysis. Teabags from 6 popular brands were investigated and categorized into three classes based on spectral unmixing and target detection results: 1) the plastic teabag primarily made of nylon 6/6; 2) those made of a composite with various polypropylene and cellulose ratios; 3) biodegradable teabags free from any plastic traces. Results demonstrated the presence of numerous plastic particles in the beverage obtained after steeping nylon teabags, but the release of particles was further amplified after microwave treatment. Nevertheless, target detection results obtained from Fourier transform infrared imaging (4000-675 cm^-1) dataset evidenced that a considerable proportion of particle residues detected were the contaminants obtained from tea granules that adsorbed on the teabag. This work highlights the significant importance of performing rigorous spectral analysis for chemical characterization, which is lacking in most published microplastic studies.

The use of a common environment for processing different powder foods in the industry has increased the risk of finding peanut traces in powder foods. The analytical methods commonly used for detection of peanut such as enzyme-linked immunosorbent assay (ELISA) and real-time polymerase chain reaction (RT-PCR) represent high specificity and sensitivity but are destructive and time-consuming, and require highly skilled experimenters. The feasibility of NIR hyperspectral imaging (HSI) is studied for the detection of peanut traces down to 0.01% by weight. A principal-component analysis (PCA) was carried out on a dataset of peanut and flour spectra. The obtained loadings were applied to the HSI images of adulterated wheat flour samples with peanut traces. As a result, HSI images were reduced to score images with enhanced contrast between peanut and flour particles. Finally, a threshold was fixed in score images to obtain a binary classification image, and the percentage of peanut adulteration was compared with the percentage of pixels identified as peanut particles. This study allowed the detection of traces of peanut down to 0.01% and quantification of peanut adulteration from 10% to 0.1% with a coefficient of determination ( r²) of 0.946. These results show the feasibility of using HSI systems for the detection of peanut traces in conjunction with chemical procedures, such as RT-PCR and ELISA to facilitate enhanced quality-control surveillance on food-product processing lines.